Forecasting demand for products

ABSTRACT

A global demand forecast is generated for a product group in a computer system, where the product group comprises a plurality of products of a predefined genre. A historical relationship between an aggregate of product level demand forecasts and actual demand for the products in the product group, and the global demand forecast are used to adjust a critical ratio employed to generate a product level demand forecast in the computer system for a product in the product group. The critical ratio expresses a probability that the product level demand forecast will exceed an actual demand for a corresponding product.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a divisional application of U.S. patent application Ser. No.11/862,062, filed Sep. 26, 2007 entitled “FORECASTING DEMAND FORPRODUCTS” which is incorporated herein by reference in its entirety.

BACKGROUND

Merchants such as brick and mortar retailers or online stores oftenrelease new products for purchase by the public. Such merchants usuallytry to store sufficient quantities of new products in inventory so as tobe able to supply the consumer demand for such products. However, giventhat there is typically little or no data that indicates what the demandwill be for new products that have yet to be released for purchase bythe public, the quantity of product that needs to be stocked ininventory to meet the demand upon initial release can be somewhatspeculative. Unfortunately, to ensure adequate inventory to meetconsumer demand, merchants often overstock a product that is to bereleased in the future. This translates into unwanted excess inventorycosts and other costs.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the invention can be better understood with reference tothe following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present invention. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a network having a plurality of clients and aserver that facilitates order fulfillment and inventory planningaccording to an embodiment of the present invention;

FIG. 2 is a curve that depicts a historical ratio of an aggregate ofproduct level demand forecasts to an aggregate demand as a function of acritical ratio that is employed to generate a demand forecast in theserver of FIG. 1 according to an embodiment of the present invention;

FIG. 3 is a flow chart of a product level forecast adjustor implementedin the server of FIG. 1 in order to adjust product level forecastsaccording to an embodiment of the present invention; and

FIG. 4 is a schematic block diagram of one embodiment of the server ofFIG. 1 according to an embodiment of the present invention.

DETAILED DESCRIPTION

With reference to FIG. 1, shown is a networked environment 100 thatincludes, for example, at least one server 103 and one or more clients106. The server 103 may represent multiple servers that may be arrangedto work in coordination with each other. Alternatively, such servers 103may be arranged in some other manner, as can be appreciated. The clients106 are configured to access information on the server 103. In oneembodiment, the clients 106 include a browser 107 to gain browser accessto the various applications in the server 103, where the server includesa web application or other appropriate application to provide suchbrowser access. Both the server 103 and the clients 106 are coupled to anetwork. The network 109 may comprise, for example, any type of networkenvironment such as the internet, intranets, local area networks, widearea networks, wireless networks, or other networks as can beappreciated.

According to various embodiments, the server 103 includes variousapplications that are executed, for example, to effect order fulfillmentfor a merchant such as an on-line merchant or a brick and mortar retailoutlet. To this end, various enterprise management systems 113 areexecuted in the server(s) 103 such as order fulfillment systems 114,inventory planning/control systems 115, or other systems as can beappreciated. The order fulfillment systems 114 provide for receivingorders from individuals or organizations that are then fulfilled usingfulfillment centers as can be appreciated. In this respect, the orderfulfillment systems 114 may leverage web applications to provide browseraccess to the clients 106 to various product ordering applications, etc.

In addition, the inventory planning/control systems 115 are executed toinsure proper levels of inventory of various products are maintained sothat consumer demand for such products can be met. There may be manyother functions that are executed on the server(s) 103 beyond thosediscussed herein as can be appreciated. However, such systems are notdescribed herein in detail as they are not particularly pertinent to thevarious embodiments of the present invention as will be described.

According to various embodiments, other applications that areimplemented on the servers 103 include a product level forecastgenerator 116 and a critical ratio adaptor 119. The server 103 alsoincludes a data store 126 within which data relating to the operation ofthe online merchant is stored. Such information may include the datathat is used to maintain the inventory of products offered by themerchant. In addition, such data includes forecasting data that is usedto determine future demand for products offered by the merchant.

To this end, the data stored in the data store 126 includes historicalaggregate product level forecast data 133, product historical demanddata 136, and a historical performance lookup table 139. In addition, itis understood that other data may be stored in the data store 126.

The historical aggregate product level forecast data 133 involves anaggregation of product level demand forecasts for products that existwithin a product group. For example, a product group may comprise acategory or type of products such as “books” or “electronics.” Thishistorical aggregate product level forecast data 133 involves anaggregation of product level demand forecasts for each of the productsin a group that have been generated and stored over time. Such productlevel demand forecasts may be generated periodically over time toprovide guidance as to the levels of inventory of various products thatshould be maintained in order to meet customer demand. The historicalaggregate product level forecast data 133 involves an aggregation ofsuch product level demand forecasts for all products within a productgroup.

The product historical demand data 136 involves the data associated withactual sales of products. Specifically, over time as products are sold,the quantities of such products sold is stored. Such data may be storedin the data store 126 as part of an inventory control function as can beappreciated.

The historical performance lookup table 139 expresses a value of anhistorical ratio as a function of a critical ratio. The critical ratiois a value that expresses the probability that a given forecast willexceed a demand for a given product. The historical ratio comprises anaggregate of the product level demand forecasts over actual productdemand for a predefined period of time. Further description of thehistorical performance lookup table 139 is described in particularitywith respect to later figures.

In addition, there are various inputs to the product level forecastgenerator 116 in order to generate a product level demand forecast 143for respective products that is employed by the inventoryplanning/control systems 115 to maintain optimum quantities of productsin inventory.

The product level forecast generator 116 is configured to generate aproduct level demand forecast 143 that is applied to the inventoryplanning/control systems 115. The product level forecast generator 116may use various approaches to generate the product level demandforecasts 143. For example, the initial product level demand forecasts143 may be generated using such techniques as time series modelingexamining demand for the product, historical price changes, and otherinformation as can be appreciated.

According to one embodiment of the present invention, the product leveldemand forecast 143 is generated at an adapted critical ratio 142 thatis determined by the critical ratio adaptor 119. Inputs to the criticalratio adaptor include a desired critical ratio 146 determined byinventory planners and is used to calculate the product level demandforecast 143. By specifying the desired critical ratio 146 in thismanner, inventory planners indicate the desired probability that a givenproduct level demand forecast 143 will exceed a demand for a givenproduct. Thus, the critical ratio comprises a value between 0 and 1. Ifplanners want to ensure that demand will not exceed inventory, forexample, then a relatively high desired critical ratio may be specifiedsuch as “0.9” or other value. Lower values for the critical ratio may bearound 0.6 or other value.

Another input into the critical ratio adaptor 119 is a global demandforecast 149. The global demand forecast 149 is a forecast of consumerdemand for a given group of products. For example, the global demandforecast 149 may involve determining consumer demand for specificproducts such as books or electronics. Also, the global demand forecast149 may involve specific demand for subsets of books such as “mysteries”or other subsets as can be appreciated. The global demand forecast 149is a “top down” forecast in that it is generated with the entiregrouping of products in mind. In this respect, various approaches may beemployed to generate such a forecast including, for example, techniquesthat are similar to those used to generate the product level demandforecasts 143.

Next, a general description of the operation of the various componentswithin the server 103 are described. To begin, the desired criticalratio 146 and the global demand forecast 149 are provided to thecritical ratio adapter 119. The critical ratio adapter 119 employs thehistorical performance lookup table 139 in order to determine whetherthe desired critical ratio 146 needs to be changed or adapted for thegeneration of product level demand forecasts 143 that comport withhistorical data based upon the current global demand forecast 149.Ultimately, the critical ratio adapter 119 generates the adaptedcritical ratio 142.

The adapted critical ratio 142 may be the desired critical ratio 146 ifthe desired critical ratio 146 comports with historical data asdetermined using the current global demand forecast 149 as will bedescribed. Alternatively, the adapted critical ratio 142 may be anadjusted value that differs from the desired critical ratio 146 as willbe described.

Once the adapted critical ratio 142 is determined, then it is providedto the product level forecast generator 116. The product level forecastgenerator 116 may then employ the adapted critical ratio 142 to generateproduct level demand forecasts 143 for all of the products within agiven product group. The product level demand forecasts 143 are thenprovided to an inventory planning/control system 115 that uses theproduct level demand forecasts 143 to determine the various quantitiesof products to be stocked in inventory.

Referring next to FIG. 2, shown is a graph that expresses a ratio ofhistorical aggregate product level demand forecasts over aggregatedemand for products as a function of critical ratio. A merchant mayobtain and store product level demand forecasts for all of the productsthat they sell to consumers over time. Since consumer demand forrespective products can change as time progresses, a merchant mightendeavor to generate product level forecasts periodically over time togauge consumer demand for products, thereby allowing a merchant tomaintain proper levels of inventory of such products. The maintenance ofinventory as such is expensive. Therefore, it is incumbent uponmerchants to ensure that they can minimize the amount of inventorymaintained, while at the same time meeting consumer demands forproducts.

Consequently, product level forecasts for the respective productsoffered by a merchant can be generated periodically. In one example,product level demand forecasts 143 may be generated for the respectiveproducts of a given merchant every three weeks or at some other timeinterval. For that matter, the forecasts may be generated every day toproject consumer demand three weeks into the future.

For a given time period, the product level demand forecasts 143associated with products in a product group may be added together andstored as a historical aggregate product level demand forecast 133 (FIG.1). According to one embodiment, the product level demand forecasts 143are generated for each product in the product group at a range of valuesfor the critical ratio. Thus, for each time period, a historicalaggregate product level demand forecast 133 is generated at severaldifferent values for the critical ratio. As set forth above, thecritical ratio expresses the probability that a given forecast willexceed a demand for the given product within the time period.

In addition, as products are sold, the quantities of products sold maybe stored as the product historical demand data 136 for the same timeperiod(s). Thus, to generate the chart of FIG. 2, the product leveldemand forecasts 143 generated at a respective critical ratio forproducts within a given product group as set forth above are aggregatedto obtain an aggregate product level demand forecast. Also, theaggregate of the demand for each individual product within therespective product group is calculated based upon the product historicaldemand data 136 for the same time period. The ratio is then calculatedfor a given value of the critical ratio as:

${Ratio} = {\frac{{Aggregate}\mspace{14mu}{Product}\mspace{14mu}{Level}\mspace{14mu}{Demand}\mspace{14mu}{Forecast}}{{Aggregate}\mspace{14mu}{Demand}}.}$

Thus, the values of the ratio above are plotted in the chart of FIG. 2as a function of the critical ratio. The plotted points may be averagedinto a curve from which the historical performance lookup table 139 isgenerated. Thus, according to one embodiment, the historical performancelookup table 139 provides a ratio of aggregate product level demandforecasts over aggregate demand for a given time period as a function ofthe critical ratio. Hereafter, this ratio is termed the “aggregateforecast/demand ratio.”

It has been determined that over time the aggregate forecast/demandratio should not change significantly over time for a given criticalratio. Thus, when making forecasts of customer demand for a givenproduct in the future, according to the various embodiments of thepresent invention, the aggregate forecast/demand ratio and the globaldemand forecast 149 are used to adjust the critical ratio that isemployed to calculate product level demand forecasts 143. This is doneto increase the possibility that the inventory of individual products ismaintained at optimal levels.

With reference to FIG. 3, shown is a flow chart that provides oneexample of the operation of the critical ratio adaptor 119 according toan embodiment of the present invention. Alternatively, the flow chart ofFIG. 3 may be viewed as depicting steps of an example of a methodimplemented in the server 103 to adapt a desired value of the criticalratio based upon the historical aggregate forecast/demand ratio and theglobal demand forecast 149. The functionality of the critical ratioadaptor 119 as depicted by the example flow chart of FIG. 3 may beimplemented, for example, in an object oriented design or in some otherprogramming architecture. Assuming the functionality is implemented inan object oriented design, then each block represents functionality thatmay be implemented in one or more methods that are encapsulated in oneor more objects. The critical ratio adaptor 119 may be implemented usingany one of a number of programming languages such as, for example, C,C++, JAVA, or other programming languages.

Beginning with box 203, the critical ratio adapter 119 inputs arespective product group comprising a predefined list of products, adesired critical ratio, and a relevant global demand forecast (GCF) fora respective product group as set forth above. Such information may beentered manually or stored in the data store 126 by virtue of anautomated process and made available to the critical ratio adaptor 119.

Thereafter, in box 206, the critical ratio adapter 119 obtains a valuefrom the lookup table for the value of the aggregate forecast/demandratio for the desired critical ratio 146 (FIG. 1) input in box 203. Togive a specific example, assume that a desired critical ratio 146 for aparticular product group is equal to 0.9. This results in an aggregateforecast/demand ratio of approximately 1.8 as determined from the lookuptable generated based upon the graph of FIG. 2.

Thereafter, in box 209, an aggregate product level demand forecast isgenerated at the desired critical ratio 146 for the products in therespective product group for the predefined future time period. Forexample, the aggregate of the product level demand forecasts 143 may becalculated at the desired critical ratio for products in a respectiveproduct group that are to be sold in a time period extending into thefuture such as, for example, three weeks or other time period. Thecritical ratio adaptor 119 may interface with the product level forecastgenerator to obtain the product level demand forecasts 143.

Next, in box 213, the critical ratio adapter 119 calculates a ratio ofthe aggregate product level demand forecast (or “aggregate productforecast (APF)”) over the global demand forecast (GDF) 149 which is theratio (APF/GDF). If it is assumed that the global demand forecast 149 isaccurate, then it follows that the global demand forecast 149 may beused as a parallel to the aggregate demand history used to calculate theaggregate forecast/demand ratio as described above.

If it is further assumed that the aggregate product level demandforecast is accurate, then the value of the ratio of the aggregateproduct level demand forecast over the global demand forecast 149 shouldbe approximately equal the historical aggregate forecast/demand ratiofor the respective product group. Underlying the above assumptions isthe fact that the historical aggregate forecast/demand ratio for a givenproduct group will not fluctuate significantly over time.

Next, in box 216, it is determined whether the ratio calculated in box213 falls within a tolerance associated with the respective aggregateforecast/demand ratio obtained from the lookup table 139. This reflectsthe fact that the calculated ratio of box 213 may not always be equal tothe aggregate forecast/demand ratio identified from the lookup table139. For that matter, the aggregate forecast/demand ratio can vary overtime, where the variation is typically not substantial.

However, according to various embodiments, it is desirable that theultimate value calculated in box 213 for the APF/GDF ratio shouldapproach the aggregate forecast/demand ratio obtained from the lookuptable in box 206. This is because this value is based in historical datathat has been accumulated over time and should not vary significantlyrelative to the product level demand forecasts generated for the future.Assuming that the APF/GCF ratio is outside of the tolerance associatedwith the aggregate forecast/demand ratio obtained from the lookup table139, then the critical ratio adapter 119 proceeds to box 219.

On the other hand, if the APF/GDF ratio is within the toleranceassociated with the aggregate forecast/demand ratio obtained from thelookup table 139, then the critical ratio adapter 119 proceeds to box223. Note that the tolerance associated with the values of the aggregateforecast/demand ratio from the lookup table 139 may be the same for allsuch values obtained therefrom at respective critical ratios.Alternatively, a separate lookup table may be generated that provides atolerance for corresponding values of the aggregate forecast/demandratio obtained from the lookup table 139 at respective critical ratios.

In box 219, the critical ratio adapter 119 determines an adjusted valuefor the APF/GDF ratio that falls within the tolerance associated withthe ratio obtained from the lookup table. According to one embodiment,this may be the highest or lowest value based on the tolerance that isclosest to the value calculated in box 213.

Thereafter, in box 226, an adjusted value for the critical ratio isdetermined at which the calculation of the APF value results in anadjusted value for the APF/GDF ratio that is congruous with the value ofthe aggregate forecast/demand ratio obtained from the lookup table 139.The value of this ratio is congruous with the aggregate forecast/demandratio obtained from the lookup table 139, for example, if the valuefalls within the tolerance associated with the aggregate forecast/demandratio or is actually equal to the value of the aggregate forecast/demandratio.

Recall in the example above, the aggregate forecast/demand ratio wasidentified in box 206 as 1.8 for a desired critical ratio of 0.9.Further assume a tolerance of +/−0.1 is associated with the aggregateforecast/demand ratio of 1.8 obtained from the lookup table 139. Alsoassume that the value of the APF/GDF ratio is equal to 2.2. Given thatthe aggregate forecast/demand ratio is 1.8 with a tolerance of +/−0.1,then it is clear that the APF/GDF ratio of 2.2 calculated in box 213 isoutside of the tolerance. As such, an adjusted value for the criticalratio is determined such that the APF/GDF ratio is equal to 1.9, whichis at the upper limit of the tolerance associated with the aggregateforecast/demand ratio of 1.8. This may be done by consulting a lookuptable that correlates critical ratio values to the value of the APF/GDFratio. Also, it may be done using an iterative process, or otherapproach. Once the adjusted value for the critical ratio is determinedin box 226, then the critical ratio adapter 119 proceeds to box 229. Inbox 229 the adjusted value for the critical ratio is stored for furtheruse in generating product level demand forecasts 143.

Thus, we see in the above example, the value for the APF/GDF ratio istoo high given the aggregate forecast/demand ratio as set forth in thelookup table 139. Thus, we are able to use the historical datarepresented by the aggregate forecast/demand ratios in the lookup table139 in addition to the global demand forecast 149 associated with agroup of products to ensure that the ultimate product level forecasts143 for products in the product group are in line with historical databased upon the more accurate global demand forecasts 149. Once theadjusted value of the critical ratio is stored in box 229, then thecritical ratio adapter 119 proceeds to box 231.

With reference back to box 223, assuming that the APF/GDF ratiocalculated in box 213 falls within the tolerance of the respectiveaggregate forecast/demand ratio looked up in box 206, then the desiredcritical ratio 146 is the proper critical ratio to be used to generateproduct level demand forecasts 143. As such, the “adapted critical ratio142” is the same as the desired critical ratio 146. This value is savedfor use in generating the product level demand forecasts 143 forrespective products in the given product group. Thereafter, the criticalratio adapter 119 proceeds to box 231 as shown.

In box 231, the adapted critical ratio 142 is sent to the product levelforecast generator 116 in order to generate product level demandforecasts 143 that are sent to the inventory control system 113 todetermine the levels of products that should be stored in inventory tomeet customer demand. In this sense, the “adapted critical ratio” may beequal to the desired critical ratio or an adjusted critical ratiodepending upon whether the APF/GDF ratio calculated in box 213 fallswithin a given tolerance of the aggregate forecast/demand ratio obtainedfrom the lookup table as set forth in box 206.

With reference to FIG. 4, shown is one example of a server 103 thatcomprises a computer server or equivalent device according to anembodiment of the present invention. The server 103 may include one ormore processor circuits having a processor 253 and a memory 256, both ofwhich are coupled to a local interface 259. In this respect, the localinterface 259 may comprise, for example, a data bus with an accompanyingcontrol/address bus as can be appreciated.

Stored on the memory 256 and executable by the processor 253 are variouscomponents such as a server operating system 263, the orderfulfillment/inventory control systems 113, the product level forecastgenerator 116, and the critical ratio adaptor 119. Also, the data store126 may be located in the memory 256 as can be appreciated. In addition,it is understood that many other components may be stored in the memory256 and executable by the processors 253. Also, such components mayreside in a memory that is external from the server 103 as can beappreciated.

As set forth above, a number of components are stored in the memory 256and are executable by the processor 253. In this respect, the term“executable” refers to a program file that is in a form that canultimately be run by the processor 253. Examples of executable programsmay be, for example, a compiled program that can be translated intomachine code in a format that can be loaded into a random access portionof the memory 256 and run by the processor 253, or source code that maybe expressed in proper format such as object code that is capable ofbeing loaded into a random access portion of the memory 256 and executedby the processor 253. An executable program may be stored in any portionor component of the memory 256 including, for example, random accessmemory, read-only memory, a hard drive, compact disk (CD), floppy disk,or other memory components.

The memory 256 is defined herein as both volatile and nonvolatile memoryand data storage components. Volatile components are those that do notretain data values upon loss of power. Nonvolatile components are thosethat retain data upon a loss of power. Thus, the memory 256 maycomprise, for example, random access memory (RAM), read-only memory(ROM), hard disk drives, floppy disks accessed via an associated floppydisk drive, compact discs accessed via a compact disc drive, magnetictapes accessed via an appropriate tape drive, and/or other memorycomponents, or a combination of any two or more of these memorycomponents. In addition, the RAM may comprise, for example, staticrandom access memory (SRAM), dynamic random access memory (DRAM), ormagnetic random access memory (MRAM) and other such devices. The ROM maycomprise, for example, a programmable read-only memory (PROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other like memory device.

In addition, the processor 253 may represent multiple processors and thememory 256 may represent multiple memories that operate in parallel. Insuch a case, the local interface 259 may be an appropriate network thatfacilitates communication between any two of the multiple processors,between any processor and any one of the memories, or between any two ofthe memories, etc. The processor 253 may be of electrical, optical, orof some other construction as can be appreciated by those with ordinaryskill in the art.

The server operating system 263 is executed to control the allocationand usage of hardware resources such as the memory and processing timein the server 103. In this manner, the server operating system 263serves as the foundation on which applications depend as is generallyknown by those with ordinary skill in the art.

Although the functionality of various components such as the criticalratio adaptor 119 and the product level forecast generator 116 aredescribed above with respect to FIGS. 1-3 as being embodied in softwareor code executed by general purpose hardware as discussed above, as analternative the same may also be embodied in dedicated hardware or acombination of software/general purpose hardware and dedicated hardware.If embodied in dedicated hardware, the functionality of these componentscan be implemented as a circuit or state machine that employs any one ofor a combination of a number of technologies. These technologies mayinclude, but are not limited to, discrete logic circuits having logicgates for implementing various logic functions upon an application ofone or more data signals, application specific integrated circuitshaving appropriate logic gates, programmable gate arrays (PGA), fieldprogrammable gate arrays (FPGA), or other components, etc. Suchtechnologies are generally well known by those skilled in the art and,consequently, are not described in detail herein.

The flow chart of FIG. 3 shows the functionality and operation of animplementation of the critical ratio adaptor 119. If embodied insoftware, each block may represent a module, segment, or portion of codethat comprises program instructions to implement the specified logicalfunction(s). The program instructions may be embodied in the form ofsource code that comprises human-readable statements written in aprogramming language or machine code that comprises numericalinstructions recognizable by a suitable execution system such as aprocessor in a computer system or other system. The machine code may beconverted from the source code, etc. If embodied in hardware, each blockmay represent a circuit or a number of interconnected circuits toimplement the specified logical function(s).

Although the flow chart of FIG. 3 shows a specific order of execution,it is understood that the order of execution may differ from that whichis depicted. For example, the order of execution of two or more blocksmay be scrambled relative to the order shown. Also, two or more blocksshown in succession in FIG. 3 may be executed concurrently or withpartial concurrence. In addition, any number of counters, statevariables, warning semaphores, or messages might be added to the logicalflow described herein, for purposes of enhanced utility, accounting,performance measurement, or providing troubleshooting aids, etc. It isunderstood that all such variations are within the scope of the presentinvention.

Also, where the functionality of the critical ratio adaptor 119 isexpressed in the form of software or code, it can be embodied in anycomputer-readable medium for use by or in connection with an instructionexecution system such as, for example, a processor in a computer systemor other system. In this sense, the functionality may comprise, forexample, statements including instructions and declarations that can befetched from the computer-readable medium and executed by theinstruction execution system. In the context of the present invention, a“computer-readable medium” can be any medium that can contain, store, ormaintain the network page for use by or in connection with theinstruction execution system. The computer readable medium can compriseany one of many physical media such as, for example, electronic,magnetic, optical, or semiconductor media. More specific examples of asuitable computer-readable medium would include, but are not limited to,magnetic tapes, magnetic floppy diskettes, magnetic hard drives, orcompact discs. Also, the computer-readable medium may be a random accessmemory (RAM) including, for example, static random access memory (SRAM)and dynamic random access memory (DRAM), or magnetic random accessmemory (MRAM). In addition, the computer-readable medium may be aread-only memory (ROM), a programmable read-only memory (PROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other type of memory device.

It should be emphasized that the above-described embodiments of thepresent invention are merely possible examples of implementations,merely set forth for a clear understanding of the principles of theinvention. Many variations and modifications may be made to theabove-described embodiment(s) of the invention without departingsubstantially from the spirit and principles of the invention. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and the present invention and protected bythe following claims.

1. A system for forecasting demand for a product, comprising: aprocessor circuit having a processor and a memory; forecasting logicstored in the memory and executable by the processor, the forecastinglogic comprising: logic that determines a value of an aggregation of aplurality of product level demand forecasts for a respective pluralityof products in a product group at a desired value of a critical ratio,the critical ratio expressing a probability that a forecast will exceeda demand for a given product; logic that determines a forecast ratio ofthe aggregation of the product level demand forecasts to a global demandforecast for the product group; logic that verifies that the forecastratio is congruous with a historical ratio of past aggregate productlevel demand forecasts to actual product demand, where the forecastratio is congruous with the historical ratio if the forecast ratio fallswithin a predefined tolerance of the historical ratio; logic thatdetermines an adjusted value of the critical ratio at which acalculation of the aggregation of the plurality of product level demandforecasts results in an adjusted value for the forecast ratio that iscongruous with the historical ratio if the forecast ratio is notverified to be congruous with the historical ratio; and logic thatprovides a product level demand forecast to an inventory control system,where the inventory control system is configured to control a quantityof at least one product in an inventory based upon the product leveldemand forecast, the product level demand forecast being determined atthe desired value of the critical ratio if the forecast ratio isverified to be congruous with the historical ratio, and the productlevel demand forecast being determined at the adjusted value of thecritical ratio if the forecast ratio is not verified to be congruouswith the historical ratio.
 2. The system of claim 1, further comprisinga lookup table stored in the memory that expresses the historical ratioas a function of the critical ratio.
 3. A method for forecasting demandfor a product, comprising: generating a global demand forecast for aproduct group in a computer system, the product group comprising aplurality of products of a predefined genre; using a historicalrelationship between an aggregate of product level demand forecasts andactual demand for the products in the product group, and the globaldemand forecast to adjust a critical ratio employed to generate aproduct level demand forecast in the computer system for a product inthe product group; and the critical ratio expressing a probability thatthe product level demand forecast will exceed an actual demand for acorresponding product.
 4. The method of claim 3, where the criticalratio is adjusted from a desired critical ratio specified for thecalculation of the product level demand forecast.
 5. The method of claim3, where the historical relationship is expressed in a curve as afunction of the critical ratio.
 6. The method of claim 3, where thehistorical relationship further comprises a forecast ratio of anaggregation of product level demand forecasts for the products in theproduct group relative to an actual demand for the products in theproduct group over a predefined time period of time in the past.
 7. Themethod of claim 3, where a plurality of aggregations of product leveldemand forecasts are generated for the predefined period of time at aplurality of values of the critical ratio, the critical ratio expressinga probability that each one of the product level demand forecasts willexceed the actual demand for the corresponding product.
 8. The method ofclaim 3, wherein the product level demand forecast is employed todetermine a level of the product that is to be stored in inventory tomeet a consumer demand for the product.
 9. A system for forecastingdemand for a product, comprising: a processor circuit having a processorand a memory; forecasting logic stored in the memory and executable bythe processor, the forecasting logic comprising: logic that determines avalue of an aggregation of a plurality of product level demand forecastsfor a respective plurality of products in a product group at a desiredvalue of a critical ratio, the critical ratio expressing a probabilitythat a forecast will exceed a demand for a given product; logic thatdetermines a forecast ratio of the aggregation of the product leveldemand forecasts to a global demand forecast for the product group;logic that determines whether the forecast ratio falls within apredefined tolerance of a historical ratio of past aggregate productlevel demand forecasts to actual product demand; and logic thatdetermines an adjusted value of the critical ratio at which acalculation of the aggregation of the plurality of product level demandforecasts results in an adjusted value for the forecast ratio that iscongruous with the historical ratio if the forecast ratio does not fallwithin the predefined tolerance of the historical ratio of pastaggregate product level demand forecasts to actual product demand. 10.The system of claim 9, wherein the forecasting logic further compriseslogic that provides a product level demand forecast to an inventorycontrol system, where the inventory control system is configured tocontrol a quantity of at least one product in an inventory based uponthe product level demand forecast, the product level demand forecastbeing determined at the desired value of the critical ratio if theforecast ratio does not fall within the predefined tolerance of thehistorical ratio of past aggregate product level demand forecasts toactual product demand.
 11. The system of claim 9, wherein theforecasting logic further comprises logic that provides a product leveldemand forecast to an inventory control system, where the inventorycontrol system is configured to control a quantity of at least oneproduct in an inventory based upon the product level demand forecast,the product level demand forecast being determined at the adjusted valueof the critical ratio if the forecast ratio does not fall within thepredefined tolerance of the historical ratio of past aggregate productlevel demand forecasts to actual product demand.
 12. The system of claim9, further comprising a lookup table stored in the memory that expressesthe historical ratio as a function of the critical ratio.
 13. A systemfor forecasting demand for a product, comprising: a processor circuithaving a processor and a memory; an application embodied in the memoryand executable by the processor circuit, the application comprising:logic that generates a global demand forecast for a product group, theproduct group comprising a plurality of products included in one of aplurality of predefined product categories; logic that uses a historicalrelationship between an aggregate of product level demand forecasts andactual demand for the products in the product group, and the globaldemand forecast to adjust a critical ratio employed to generate aproduct level demand forecast for a product in the product group; andwherein the critical ratio expresses a probability that the productlevel demand forecast will exceed an actual demand for a correspondingproduct.
 14. The system of claim 13, where the critical ratio isadjusted from a desired critical ratio specified for the calculation ofthe product level demand forecast.
 15. The system of claim 13, where thehistorical relationship is expressed in a curve as a function of thecritical ratio.
 16. The system of claim 13, where the historicalrelationship further comprises a forecast ratio of an aggregation ofproduct level demand forecasts for the products in the product grouprelative to an actual demand for the products in the product group overa predefined time period of time in the past.
 17. The system of claim13, where a plurality of aggregations of product level demand forecastsare generated for the predefined period of time at a plurality of valuesof the critical ratio, the critical ratio expressing a probability thateach one of the product level demand forecasts will exceed the actualdemand for the corresponding product.
 18. The system of claim 13,wherein the product level demand forecast is employed to determine alevel of the product that is to be stored in inventory to meet aconsumer demand for the product.
 19. The system of claim 13, furthercomprising a lookup table stored in the memory that expresses ahistorical ratio as a function of the critical ratio, the historicalratio expressing a forecast ratio of a plurality of past aggregateproduct level demand forecasts to the actual demand for the products inthe product group.
 20. The system of claim 13, wherein the applicationis configured for periodic execution.